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Related Experiment Video

Updated: May 9, 2025

Design and Application of a Fault Detection Method Based on Adaptive Filters and Rotational Speed Estimation for an Electro-Hydrostatic Actuator

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Model predictive motion/force control in robotic grinding system for turbine blade.

Ziling Wang¹, Lai Zou¹, Jiantao Li¹

¹State Key Laboratory of Mechanical Transmission for Advanced Equipment, Chongqing University, Chongqing 400044, China.

ISA Transactions

|April 30, 2025

Summary

This study introduces a model predictive motion/force control (MPMFC) algorithm to improve robot grinding accuracy. The MPMFC algorithm enhances control of robot trajectory and contact force, leading to superior grinding quality for complex parts.

Keywords:

Model predictive control Online trajectory interpolation method Robot motion/force control Robotic grinding system Turbine blade

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Area of Science:

Robotics
Manufacturing Engineering
Control Systems

Background:

Robot grinding systems suffer from unstable contact forces and position errors due to nonlinear dynamics and low motion accuracy.
These issues significantly degrade grinding quality, especially for complex curved workpieces like turbine blades.

Purpose of the Study:

To propose a model predictive motion/force control (MPMFC) algorithm for precise control of robot trajectory and contact force in grinding applications.
To enhance the grinding quality of complex curved workpieces by addressing unstable contact forces and position errors.

Main Methods:

Implemented an online trajectory interpolation method for acquiring ideal end-effector trajectory information within a control cycle.
Developed an MPMFC system using optimal control, incorporating an admittance model for contact force prediction and a robot kinematics model for motion prediction.

Main Results:

The MPMFC algorithm demonstrated superior performance in simulations and experiments.
Achieved force control accuracy of 1.934 N and position control accuracy of 0.132 mm during turbine blade tracking.
Showcased improvements exceeding 38% in force control and 37% in position control compared to conventional methods.

Conclusions:

The proposed MPMFC algorithm effectively addresses challenges in robot grinding, particularly for complex geometries.
The algorithm significantly enhances both force and position control accuracy, leading to improved grinding quality.
MPMFC represents a significant advancement for precise robotic grinding operations.